This hypothetical design shows how an AI agent could classify support requests, retrieve approved guidance, propose routing or responses, and request human approval before sensitive actions. It does not describe a completed deployment or measured result.
A service operation receives requests through several channels, while agents must interpret intent, find relevant guidance, route cases, and identify issues requiring specialist attention.
This blueprint separates classification, retrieval, recommendation, and tool use so each decision can receive its own evidence and authority limit before any live support action is considered.
Channel adapters for approved request sources
Workflow orchestrator with state and policy controls
Retrieval service connected to governed knowledge
Agent tool gateway linked to case-management functions
Audit, monitoring, and human-review services
Map request types, policies, handoffs, and exceptions
Define taxonomy, authority limits, and evaluation cases
Build a restricted prototype without production actions
Run reviewed tests and failure simulations
Plan a monitored pilot with explicit release criteria
No classification threshold, handling-time change, or automation rate is asserted. A pilot would set acceptance criteria from the reviewed support taxonomy and action policy.
Bring the request channels, routing policy, knowledge sources, sensitive actions, and escalation rules that would shape a controlled support assessment.